Microstructure and properties of coarse-grained WC-10Co cemented carbides with different carbon contents during heat treatments

The effects of cryogenic treatment(CT)and tempering-cryogenic treatment(TCT)on the microstructure and properties of coarse-grained WC-10Co cemented carbides with different carbon contents were researched.The binder phase,WC mean grain sizes,W solubility in the binder,relative magnetic s aturation,densities,hardness,wear resistance and second phase precipitation of cemented carbides with different heat treatments were discussed.The results show that there are significant changes of microstructure and properties in the samples with CT and TCT,especially due to the precipitation of metastable nanoparticles W_(x)Co_(y)C_(z) in the binder during the heat treatments of CT and TCT.With the simultaneous combination of microstructure and nanoparticle-reinforced binder,a dramatically improved combination of hardness and wear resistance of the samples after TCT has been achieved.

Influence of Carbon Content on Element Diffusion in Silicon Carbide-Based TRISO Composite Fuel

The coating layers of Tri-structural Isotropic Particles(TRISO)serve to protect the kernel and act as barriers to fission products.Sintering aids in the silicon carbide matrix variably react with TRISO coating layers,leading to the destruction of the coating layers.Investigating how carbon content affects element diffusion in silicon carbide-based TRISO composite fuel is of great significance for predicting reactor safety.In this study,silicon carbide-based TRISO composite fuels with different carbon contents were prepared by adding varying amounts of phenolic resin to the silicon carbide matrix.X-ray Diffraction(XRD)and Scanning Electron Microscopy(SEM)were employed to characterize the phase composition,morphology,and microstructure of the composite fuels.The elemental content in each coating layer of TRISO was quantified using Energy-Dispersive X-ray Spectroscopy(EDS).The results demonstrated that the addition of phenolic resin promoted the uniform distribution of sintering aids in the silicon carbide matrix.The atomic percentage(at.%)of aluminum(Al)in the pyrolytic carbon layer of the TRISO particles reached its lowest value of 0.55%when the phenolic resin addition was 1%.This is because the addition of phenolic resin caused the Al and silicon(Si)in the matrix to preferentially react with the carbon in the phenolic resin to form a metastable liquid phase,rather than preferentially consuming the pyrolytic carbon in the outer coating layer of the TRISO particles.The findings suggest that carbon addition through phenolic resin incorporation can effectively mitigate the deleterious reactions between the TRISO coating layers and sintering aids,thereby enhancing the durability and safety of silicon carbide-based TRISO composite fuels.

Total organic carbon content logging prediction based on machine learning:A brief review

The total organic carbon content usually determines the hydrocarbon generation potential of a formation.A higher total organic carbon content often corresponds to a greater possibility of generating large amounts of oil or gas.Hence,accurately calculating the total organic carbon content in a formation is very important.Present research is focused on precisely calculating the total organic carbon content based on machine learning.At present,many machine learning methods,including backpropagation neural networks,support vector regression,random forests,extreme learning machines,and deep learning,are employed to evaluate the total organic carbon content.However,the principles and perspectives of various machine learning algorithms are quite different.This paper reviews the application of various machine learning algorithms to deal with total organic carbon content evaluation problems.Of various machine learning algorithms used for TOC content predication,two algorithms,the backpropagation neural network and support vector regression are the most commonly used,and the backpropagation neural network is sometimes combined with many other algorithms to achieve better results.Additionally,combining multiple algorithms or using deep learning to increase the number of network layers can further improve the total organic carbon content prediction.The prediction by backpropagation neural network may be better than that by support vector regression;nevertheless,using any type of machine learning algorithm improves the total organic carbon content prediction in a given research block.According to some published literature,the determination coefficient(R^(2))can be increased by up to 0.46 after using machine learning.Deep learning algorithms may be the next breakthrough direction that can significantly improve the prediction of the total organic carbon content.Evaluating the total organic carbon content based on machine learning is of great significance.

Non-intrusive soil carbon content quantification methods using machine learning algorithms:A comparison of microwave and millimeter wave radar sensors

Agricultural and forestry biomass can be converted to biochar through pyrolysis gasification,making it a significant carbon source for soil.Applying biochar to soil is a carbon-negative process that helps combat climate change,sustain soil biodiversity,and regulate water cycling.However,quantifying soil carbon content conventionally is time-consuming,labor-intensive,imprecise,and expensive,making it difficult to accurately measure in-field soil carbon’s effect on storage water and nutrients.To address this challenge,this paper for the first time,reports on extensive lab tests demonstrating non-intrusive methods for sensing soil carbon and related smart biochar applications,such as differentiating between biochar types from various biomass feedstock species,monitoring soil moisture,and biochar water retention capacity using portable microwave and millimeter wave sensors,and machine learning.These methods can be scaled up by deploying the sensor in-field on a mobility platform,either ground or aerial.The paper provides details on the materials,methods,machine learning workflow,and results of our investigations.The significance of this work lays the foundation for assessing carbon-negative technology applications,such as soil carbon content accounting.We validated our quantification method using supervised machine learning algorithms by collecting real soil mixed with known biochar contents in the field.The results show that the millimeter wave sensor achieves high sensing accuracy(up to 100%)with proper classifiers selected and outperforms the microwave sensor by approximately 10%–15%accuracy in sensing soil carbon content.

Carbon pool structure and carbon density of soil in Pinus koraiensis plantation ecosystem

The organic carbon contents,carbon density and carbon storage of the soil in the Pinus koraiensis plantation ecosystem were investigated in Maoershan experimental forest farm,Shangzhi County,Heilongjiang,on the west slope of the Zhangguangcai Mountains in northeastern China for providing data to evaluation of the carbon balance in forest ecosystem of northeastern China.These soil carbon indicators were measured in three forest types,pure P.koraiensis plantation,P.koraiensis and Betula platyphylla mixed forest,and the P.koraiensis and Quercus mongolica mixed forest.The soil carbon pool consisted of four compartments,namely L layer,F layer,H layer and B layer.With variance analysis,we found that both organic carbon content and carbon density of the soil were significantly affected by forest types,soil compartments and slope positions.The highest soil carbon density（278.63 Mg·ha^-1）.was observed in the mixed forest of P.koraiensis and Q.mongolica.The B layer had the highest carbon density（212.28 Mg·ha^-1） among all the soil compartments.In terms of slope position,the highest soil carbon density（394.18 Mg·ha^-1） presented in the low slope.Besides,soil carbon content and carbon density had a marked change with the organic matter content and vertical depth of the soil in each compartment.The results of this study implied that in the temperate humid region,the mixed ecosystem of regional Pinus koraiensis plantations and natural forest had relatively high carbon storage capability.

Microstructure evolution and mechanical properties of high-boron steel with different ratios of boron and carbon

Boron and carbon contents are the main factors influencing the properties of high-boron steel.In this study,experimental samples with different boron-to-carbon ratios(%B/%C)were prepared.The microstructures of the different samples were observed,and their hardness,bending strength,and impact toughness were investigated.Results show that the main microstructures in the investigated high-boron steel samples are the eutectic Fe_(2)B structure with a fishbone shape and the ternary peritectic Fe_(3)(C,B)structure with a chrysanthemum shape.When the boron content is 2.5wt.%and the carbon content is 0.43wt.%(i.e.,%B/%C=5.82),the overall mechanical properties of the alloy are the best.The alloy's hardness,bending strength and impact toughness reach their maximums,which are 67.3 HRC,1,267.36 MPa and 6.19 J·cm^(-2),respectively.The optimized alloy is compared with conventional materials exhibiting excellent wear resistance(namely,high-manganese steel and high-chromium cast iron)through two-body and three-body abrasion tests.The wear resistance of this high-boron steelinvestigated in this work is found to be superior to those of the more common materials.

Soil carbon pool in China and its global significance

Soil organic carbon density and its related characteristics of 41 soil types all over China were analyzed by using data of 745 soil profiles , and size of soil carbon pool was estimated. As a result, area-weighted averages of these 41 soil types for bulk density, profile depth, organic carbon content and profile carbon were 1. 24 tC/m3, 86. 2 cm, 3. 04% and 19. 7 kg C/m2 respectively. Total size of soil carbon pool was 185. 68 × 1009tC, which is 29 times of that in terrestrial biomass of China and 12. 6% of global soil carbon pools. Because of its huge carbon pool, soil of China plays an important role in global carbon cycle.

Soil Organic Carbon Content and Distribution in a Small Landscape of Dongguan, South China

Global warming has become an increasing concern, and using soil as a carbon sink to sequester carbon dioxide has attracted much attention in recent years. In this study, soil organic carbon (SOC) content and organic carbon density were estimated based on a soil survey of a small landscape in Dongguan, South China, with spatial heterogeneity of SOC distribution and the impacts of land-use patterns on soil organic carbon content assessed. Field sampling was carried out based on a 150 m×150 m grid system overlaid on the topographic map of the study area and samples were collected in three 20-cm layers to a depth of 60 cm. Spatial variability in the distribution of SOC was assessed using the Kruskal-Wallis test. Results showed that SOC in the topsoil layer (0-20 cm) was not much higher or even lower in some sites than the underlying layers, and except for the two sites covered with natural woodland, it did not exhibit a pronounced vertical gradient. The difference in both horizontal and vertical distribution of SOC was not statistically significant. However, in the topsoil layer among land-use/land-cover patterns, significant differences (P≤0.05) in SOC distribution existed, indicating that management practices had great impact on SOC content. SOC storage in the study area to a depth of 20, 40, and 60 cm was estimated as 2.13×106 kg, 3.46×106 kg, and 4.61×106 kg, respectively.

Spatial modeling of the carbon stock of forest trees in Heilongjiang Province, China

Heilongjiang province is the largest forest zone in China and the forest coverage rate is 46%. Forests of Heilongjiang province play an important role in the forest ecosystem of China. In this study we investi- gated the spatial distribution of forest carbon storage in Heilongjiang province using 3083 plots sampled in 2010. We attempted to fit two global models, ordinary least squares model （OLS）, linear mixed model （LMM）, and a local model, geographically weighted regression model （GWR）, to the relationship between forest carbon content and stand, environment, and climate factors. Five predictors significantly affected forest carbon storage and spatial distribution, viz. average diameter of stand （DBH）, number of trees per hectare （TPH）, elevation （Elev）, slope （Slope） and the product of precipitation and temperature （Rain Temp）. The GWR model outperformed the two global models in both model fitting and prediction because it successfully reduced both spatial auto- correlation and heterogeneity in model residuals. More importantly, the GWR model provided localized model coefficients for each location in the study area, which allowed us to evaluate the influences of local stand conditions and topographic features on tree and stand growth, and forest carbon stock. It also helped us to better understand the impacts of silvi- cultural and management activities on the amount and changes of forest carbon storage across the province. The detailed information can be readily incorporated with the mapping ability of GIS software to provide excellent tools for assessing the distribution and dynamics of the for- est-carbon stock in the next few years.

Effects of Vegetation Restoration on Soil Organic Carbon in China:A Meta-analysis

Vegetation restoration has been proposed as an effective method for increasing both plant biomass and soil carbon(C) stocks. In this study, 204 publications(733 observations) were analyzed, focusing on the effects of vegetation restoration on soil organic carbon(SOC) in China. The results showed that SOC was increased by 45.33%, 24.43%, 30.29% and 27.98% at soil depths of 0–20 cm, 20–40 cm, 40–60 cm and > 60 cm after vegetation restoration, respectively. Restoration from both cropland and non-cropland increased the SOC content. The conversion of non-cropland was more efficient in SOC accumulation than the conversion of cropland did, especially in > 40 cm layers. In addition, the conversion to planted forest led to greater SOC accumulation than that to other land use did. Conversion period and initial SOC content extended more influence on soil C accumulation as the main factors after vegetation restoration than temperature and precipitation did. The SOC content significantly increased with restoration period after long-term vegetation restoration(> 40 yr), indicating a large potential for further accumulation of carbon in the soil, which could mitigate climate change in the near future.

An optimized hardness model for carburizing-quenching of low carbon alloy steel

Research has been conducted about the hardness prediction for the carburizing and quenching process based on an optimized hardness simulation model,in accordance with the calculation rule of mixed phases.The coupling field model incorporates carburizing field analysis,temperature field analysis,phase transformation kinetics analysis and a modified hardness calculation model.In determination of the calculation model for hardness,calculation equations are given to be applied to low carbon content(x(C)<0.5%) for the child phases and the martensite hardness is calculated for high carbon content(x(C)>0.5%) in alloy.Then,the complete carburizing-quenching hardness calculation model is built,and the hardness simulation data are corrected considering the influence of residual austenite(RA) on hardness.Hardness simulations of the carburizing and quenching process of 17CrNiMo6 samples have been performed using DEFORM-HT_V10.2 and MATLAB R2013 a.Finally,a series of comparisons of simulation results and measured values show a good agreement between them,which validates the accuracy of the proposed mathematical model.

Quantitative Analysis of Carbon Content in Bituminous Coal by Laser-Induced Breakdown Spectroscopy Using UV Laser Radiation

The carbon content of bituminous coal samples was analyzed by laser-induced breakdown spectroscopy. The 266 nm laser radiation was utilized for laser ablation and plasma generation in air. The partial least square method and the dominant factor bused PLS method were used to improve the measurement accuracy of the carbon content of coal. The results showed that the PLS model could achieve good measurement accuracy, and the dominant factor based PLS model could further improve the measurement accuracy. The coefficient of determination and the root-mean-square error of prediction of the PLS model were 0.97 and 2.19%, respectively; and those values for the dominant factor based PLS model were 0.99 and 1.51%, respectively. The results demonstrated that the 266 nm wavelength could accurately measure the carbon content of bituminous coal.

Ecosystem carbon and nitrogen storage following farmland afforestation with black locust(Robinia pseudoacacia) on the Loess Plateau, China

Although afforestation of farmlands has been proposed as an effective method of carbon(C) sequestration, there remain uncertainties that deter us from developing a clear picture of C stocks in plantation ecosystems.This study investigated the dynamics of stand structure and plant diversity, and C and nitrogen(N) pools in trees,herbs, litter, and soil(0–100 cm depth) in black locust plantations aged 9, 17, 30, and 37 years, and in newly abandoned farmlands as pre-afforestation sites, on the Loess Plateau, China. Stand density decreased significantly, while tree diameter at breast height and height increased during stand development. The dominant species of the herb layer differed with age. Afforestation resulted in slight increases in tree C and N storage in plantations from 9 to 30 years of age, and then significantly increased from 30 to 37 years. Compared to pre-afforestation, C and N storage in soil decreased to minimum values in stands aged 17 and 9 years, respectively. The soil re-accumulated C and N during stand development, attaining equilibrium levels similar to those in pre-afforestation when stands reached about 30 years of age. Soil C and N storage in 37-year stands were 29 and 16% higher, respectively, than in pre-afforestation levels. However, C and N concentrations in the subsoil(20–40 cm) were still less than the preafforestation levels for stands of all ages(from 9 to 37 years). The relative contribution to the total ecosystem C and N pools increased in trees and decreased in soil during the observed period. Our results indicate that afforestation reduced soil C and N storage during the early stages of stand development. We conclude that the growing phase of an afforested stand over its initial 30 years is important for C and N sequestration by black locust due to the C and N storage that result from recovered soil quality and an increase in tree biomass.

Effects of carbon content and solidification rate on thermal conductivity of grey cast iron

The thermal conductivity or diffusivity of pearlitic grey irons with various carbon contents is investigated by the laser flash method. The materials are cast in controlled thermal environments and produced in three dissimilar cooling rates. The cooling rate together with the carbon content largely influence the thermal conductivity of grey iron. Linear relationships exist between the thermal conductivity and the carbon content,the carbon equivalent and the fraction of former primary solidified austenite transformed into pearlite. The work shows that optimal thermal transport properties are obtained at medium cooling rates. Equations describing the thermal conductivity of pearlite,solidified as pre-eutectic austenite,and the eutectic of grey iron are derived. The thermal conductivity of pearlitic grey iron is modeled at both room temperature and elevated temperature with good accuracy.

Morphology and carbon content of WC-6%Co nanosized composite powders prepared using glucose as carbon source

Pure WC-6%Co nanosized composite powders were synthesized via a low-temperature method.The effects of carbon source on microstructure characteristic of composite powders were investigated,and the effects of heat-treatment parameter on carbon content of composite powders were also discussed.The results of SEM and XRD revealed that the carbon decomposing from glucose was more active than carbon black.Therefore,WC-Co nanosized composite powders could be synthesized at 900°C for 1 h under a hydrogen atmosphere.The individual WC grains were bonded together into a long strip under the action of cobalt.The results of carbon analysis revealed that the total carbon content decreased with the increase of the temperature in the range of 800-1000°C.Moreover,the total carbon content and the compounded carbon increased with the increase of the flow rate of H2 in the range of 1.1-1.9 m3/h.

EFFECTS OF CARBON CONTENT AND ROLLING PROCESSING ON RETAINED AUSTENITE FOR HOT-ROLLED TRIP STEELS

The effects of finishing rolling temperature and coiling temperature on retained austen-ite were studied for hot-rolled transformation induced plasticity (TRIP) steels with different carbon content. The experimental results showed that an appropriate volume fraction of retained austenite from 6% to 11% could be obtained according to the different carbon content less than 0.20% by controlled finishing rolling and coiling for the hot-rolled TRIP steels. It can be concluded that carbon content has a significant effect on the fraction of retained austenite and coiling processing plays stronger role on retaining austenite than fishing rolling processing.

Reduction of Cr(Ⅵ) with a relative high concentration using different kinds of zero-valent iron powders: Focusing on effect of carbon content and structure on reducibility

Reduction of Cr(VI)using zero-valent iron(ZVI)could not only decrease the amounts of chemicals used for reduction,but also decrease the discharge of sludge.In order to find a desirable ZVI material,reduction of Cr(VI)with a relative high concentration using different kinds of ZVI powders(mainly carbon differences)including reduced Fe,grey cast iron,pig iron,nodular pig iron was carried out.Parameters such as ZVI dosage,type and size affecting on Cr(VI)reduction were firstly examined and grey cast iron was selected as a preferable reducing material,followed by pig iron.Additionally,it was found that the parameters had significant influences on experimental kinetics.Then,morphology and composition of the sample before and after reaction were characterized by SEM,EPMA and XPS analyses to disclose carbon effect on the reducibility.In order to further interpret reaction mechanism,different reaction models were constructed.It was revealed that not only the carbon content could affect the Cr(VI)reduction,but also the carbon structure had an important effect on its reduction.

Carbon Storage Capacity of Different Plantation Types Under Sandstorm Source Control Program in Hebei Province, China

Afforestation and reforestation are effective and ecological ways of mitigating elevated atmospheric carbon dioxide(CO2) concentration and increasing carbon(C) storage in terrestrial ecosystems. In this study, we measured the above-ground(tree, herbaceous plants and litter) and below-ground(root and soil) C storage in an aspen plantation(Populus davidiana) monoculture(PD), a larch plantation(Larix pincipis-rupprechtii) monoculture(LP), a pine plantation(Pinus tabulaeformis) monoculture(PT), a larch and birch mixed plantation(L. pincipis-rupprechtii and Betula platyphlla mixed)(MLB), and an apricot plantation(Armeniaca sibirica) monoculture(AS) under the Desertification Combating Program in Hebei Province, the northern China. The objective was to assess the effect of afforestation species on ecosystem C pools of different plantation types. Results showed that C storage of LP stand(258.0 Mg/ha) and MLB(163.4 Mg/ha) were significantly higher than the C storage in PD(45.5 Mg/ha), PT(58.9 Mg/ha) and AS(49.4 Mg/ha), respectively. Soil C was the main carbon pool of the ecosystem C storage in the five plantation stands, ranging from 31.4 Mg/ha to 232.5 Mg/ha, which accounted for 69.0%–90.1% of the total ecosystem C storage. The C storage in tree layer was about 5.2%–23.2% of ecosystem C storage. The herbaceous plants and litter layers contained 1.0%–6.0% and 1.5%–3.3% of ecosystem C storage, respectively. Our results suggest that tree species should be incorporated to accurately develop regional C budget of afforestation program, and also imply that substantial differences in ecosystem C stocks among plantation types can facilitate decision making on C management.

Influence of Carbon Content on S Zorb Sorbent Activity

The reaction activity of S Zorb sorbents with different sulfur contents was investigated, and the structure and composition of carbon-containing sorbents were characterized by XRD, FT-IR and TG-MS in order to delve into the kind and morphology of carbon on the sorbent. Test results have revealed that coke could be deposited on the S Zorb sorbent during the operating process, and the coke content was an important factor influencing the reaction performance of the S Zorb sorbent. Retention of a definite amount of coke on the sorbent while securing the desulfurization activity of the S Zorb sorbent would be conducive to the reduction of octane loss of reaction product.